Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths

Ting Qian

doi:10.1109/ICMLA.2007.77

Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Standard genetic algorithms (GA) are often confronted with the problem of rapid premature convergence. The loss of diversity in a population usually slows down evolution to a significant extent. In this paper, we explore the use of an original strategy called the Multi-stages GA as a means of impeding premature convergence and optimizing evolutionary progresses at the same time. The algorithm introduces the idea of temporally organizing an evolutionary process. Evaluation results show that the Multi-stages GA significantly outperforms the standard GA.

Original language	English (US)
Title of host publication	Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
Pages	56-61
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLA.2007.77
State	Published - 2007
Externally published	Yes
Event	6th International Conference on Machine Learning and Applications, ICMLA 2007 - Cincinnati, OH, United States Duration: Dec 13 2007 → Dec 15 2007

Publication series

Name	Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007

Conference

Conference	6th International Conference on Machine Learning and Applications, ICMLA 2007
Country/Territory	United States
City	Cincinnati, OH
Period	12/13/07 → 12/15/07

All Science Journal Classification (ASJC) codes

Computer Science Applications
Human-Computer Interaction
Control and Systems Engineering

Access to Document

10.1109/ICMLA.2007.77

Cite this

Qian, T. (2007). Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths. In Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007 (pp. 56-61). Article 4457208 (Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007). https://doi.org/10.1109/ICMLA.2007.77

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title = "Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths",

abstract = "Standard genetic algorithms (GA) are often confronted with the problem of rapid premature convergence. The loss of diversity in a population usually slows down evolution to a significant extent. In this paper, we explore the use of an original strategy called the Multi-stages GA as a means of impeding premature convergence and optimizing evolutionary progresses at the same time. The algorithm introduces the idea of temporally organizing an evolutionary process. Evaluation results show that the Multi-stages GA significantly outperforms the standard GA.",

author = "Ting Qian",

year = "2007",

doi = "10.1109/ICMLA.2007.77",

language = "English (US)",

isbn = "0769530699",

series = "Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007",

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note = "6th International Conference on Machine Learning and Applications, ICMLA 2007 ; Conference date: 13-12-2007 Through 15-12-2007",

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Qian, T 2007, Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths. in Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007., 4457208, Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007, pp. 56-61, 6th International Conference on Machine Learning and Applications, ICMLA 2007, Cincinnati, OH, United States, 12/13/07. https://doi.org/10.1109/ICMLA.2007.77

Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths. / Qian, Ting.
Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007. 2007. p. 56-61 4457208 (Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Qian T. Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths. In Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007. 2007. p. 56-61. 4457208. (Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007). doi: 10.1109/ICMLA.2007.77

Multi-stages genetic algorithms: Introducing temporal structures to facilitate selection of optimal evolutionary paths

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All Science Journal Classification (ASJC) codes

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